6,684 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference
The process of automatic generation of a road map from GPS trajectories,
called map inference, remains a challenging task to perform on a geospatial
data from a variety of domains as the majority of existing studies focus on
road maps in cities. Inherently, existing algorithms are not guaranteed to work
on unusual geospatial sites, such as an airport tarmac, pedestrianized paths
and shortcuts, or animal migration routes, etc. Moreover, deep learning has not
been explored well enough for such tasks. This paper introduces COLTRANE,
ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference framework which
operates on GPS trajectories collected in various environments. This framework
includes an Iterated Trajectory Mean Shift (ITMS) module to localize road
centerlines, which copes with noisy GPS data points. Convolutional Neural
Network trained on our novel trajectory descriptor is then introduced into our
framework to detect and accurately classify junctions for refinement of the
road maps. COLTRANE yields up to 37% improvement in F1 scores over existing
methods on two distinct real-world datasets: city roads and airport tarmac.Comment: BuildSys 201
Automatic Inference of Roadmaps from Raw Mobility Traces
The popularization of smartphones in daily life offers numerous opportunities in terms of urban sensing. More and more users are ready to share certain information as part of scientific research, including their GPS location. From these mobility traces, we developed a roadmap inference algorithm using raw mobile data supplied by users of smartphones. This algorithm can generate a map composed of oriented routes, which are annotated by a certain amount of metadata.Le succès des smartphones dans la vie quotidienne ajoute de nombreuses opportunités en matière de collecte de données urbaines. De plus en plus d'utilisateurs sont prêts à partager certaines données dans le cadre de recherches scientifiques, notamment leurs positions GPS. À partir de telles traces de mobilités, nous avons développé un algorithme d'inférence de cartes routières à partir de données mobiles brutes, fournies par des utilisateurs de smartphones. Cet algorithme permet de générer une carte routière composée de routes orientées, ainsi qu'un certain nombre d'informations sur celles-ci
Identifying and Tracking Pedestrians Based on Sensor Fusion and Motion Stability Predictions
The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle
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